Obesity is a pervasive public health problem, yet most effective weight loss treatments have been in-person Intensive Lifestyle Interventions (INLIs) that are too costly and burdensome to scale. Recently though, an important comparative effectiveness trial conducted by our consultant, Lawrence Appel demonstrated that a technology-supported weight loss INLI that delivered lessons via the web and coaching via telephone could be as effective as in-person treatment. Those findings suggest that it may be feasible to develop more resource- efficient obesity treatment than was previously believed possible. We propose to apply an innovative methodological framework, the Multiphase Optimization Strategy (MOST) 14, to improve a remotely delivered, technology-supported intensive lifestyle intervention for obesity. Unlike nearly all previous research which treats INLI as a bundled treatment package, MOST implements efficient experimentation on individual intervention components to determine which could be reduced, eliminated, or replaced to improve efficiency. This information then guides assembly of an optimal, comprehensive treatment package that, when combined with a core intervention (CORE), achieves target outcomes with least resource consumption and participant burden. The proposed experiment quantifies the effects of five experimental treatment components on 6 month weight loss among 560 adults. The components are: 1. Number of coaching calls [24 or 12]; 2. Text Message support [yes or no]; 3. Primary Care Provider (PCP) contact [yes or no]; 4. Buddy training [yes or no]; 5. Meal replacements recommended [yes or no]). The five components were selected to foster adherence to obesity treatment by acting upon social cognitive mediators of behavior change that are posited by social-cognitive theory (self-efficacy, self-regulation, supportive accountability, facilitation). Findings will be applied to build a new Opt-IN intervention made up of only active components and implementable for $500 or less. Because such an intervention will be both effective and scalable, it will enjoy greatly increased reach and make significant progress in the fight against obesity.